Head of Applied AI

Physical Superintelligence is a startup with roots at Google, NVIDIA, Harvard, Meta, MIT, Oxford, Johns Hopkins, Cambridge, and the Perimeter Institute building AI systems to discover new physics at scale.

Our mission is to discover and commercialize transformative physics breakthroughs at scale with artificial superintelligence, safely, verifiably, and for broad public benefit.

The last century's golden age of physics gave us transistors, lasers, and nuclear energy. We believe artificial superintelligence will unlock the next one. We're creating the infrastructure to industrialize scientific discovery and usher in this new era.

We have one product: new physics, at scale.

• Lead Applied AI, the organization that solves customers' engineering problems with our models, simulations and agents: own the people, the results and the hiring.

• Set the technical direction: where a classical solver is required, where a learned model is enough, how both are validated, and when a result is good enough for a customer.

• Own customer delivery end to end, scoped around the customer's actual problem, shipped into their environment, leaving a reusable capability behind.

• Be the technical counterpart to go-to-market: decide what we can credibly promise and own the escalation when an engagement is at risk.

• Stay technical: carry individual work on engagements and set the bar by shipping working science.

• At least three years of direct people management, hiring, performance reviews, and career development for the people who reported to you, and a track record of building a function: you have hired and led a customer-facing technical team from small or from scratch, and the function outlived your direct involvement in every deal.

• Eight or more years of work applying ML and simulation to engineering, industrial, or physics problems, delivered to customers or partners at a company known for technical rigor. You have modeled a physical system that mattered, validated it against ground truth, and can say how you knew it was right. Recommenders, ads, and business analytics do not count toward this.

• Depth in one of the following engineering domains, in the vertical you delivered to. You can build a model, debug a pipeline, and reason about accuracy, speed, and extrapolation risk with a senior domain engineer.

  • Fluid dynamics and heat transfer: CFD, conjugate heat transfer, multiphase and two-phase flow, natural and forced convection.

  • Thermodynamics and energy conversion: refrigeration and power cycles, heat exchangers, combustion, engines and turbines, HVAC.

  • Electrical and power systems: power flow, interconnection and protection, power electronics and converter dynamics, electromagnetics.

  • Electrochemistry and energy storage: batteries, fuel cells, corrosion.

  • Solid mechanics and materials: FEA, vibration, fatigue, materials degradation.

  • Transport and environmental physics: atmospheric dispersion, hydrology and water systems, subsurface flow, fire.

  • Chemical and process engineering: reaction kinetics, process simulation, working fluids.

Motivation for this specific job is assessed at the first conversation. This role is commercial problem-solving with real science, and we will ask for the last commercial problem you personally found interesting and why.

• A PhD, or an equivalent research record, in engineering, applied physics, or computational science.

• Experience owning the tools or interfaces through which technical work reaches its users, or turning bespoke engagements into a product.

• Depth in a second lane from the list above.

• Domain experience in energy, infrastructure, or other industrial verticals.

• Forward-deployed or solutions leadership at a company that scaled the…